Detection of the local adaptive and genome-wide associated loci in southeast Nigerian taro (Colocasia esculenta (L.) Schott) populations.

BACKGROUND: Taro has a long history of being consumed and remains orphan and on the hand Nigeria farmers. The role of farmer-driven artificial selection is not negligible to fit landraces to a particular ecological condition. Limited study has been conducted on genome-wide association and no study has been conducted on genome-environment association for clinal adaptation for taro. Therefore, the objective of this study was to detect loci that are associated with environmental variables and phenotype traits and forward input to breeders. The study used 92 geographical referred taro landraces collected from Southeast (SE) Nigeria. RESULTS: The result indicates that SE Nigerian taro has untapped phenotype and genetic variability with low admixture. Redundancy analysis indicated that collinear explained SNP variation more than single climatic variable. Overall, the results indicated that no single method exclusively was able to capture population confounding effects better than the others for all six traits. Nevertheless, based on overall model performance, Blink seemed to provide slight advantage over other models and was selected for all subsequent assessment of genome-environment association (GEA) and genome-wide association study (GWAS) models. Genome scan and GEA identified local adapted loci and co-located genes. A total of nine SNP markers associated with environmental variables. Some of the SNP markers (such as S_101024366) co-located with genes which previously reported for climatic adaptation such as astringency, diaminopimelate decarboxylase and MYB transcription factor. Genome-wide association also identified 45, 40 and 34 significant SNP markers associated with studied traits in combined, year 1 and year 2 data sets, respectively. Out of these, five SNP markers (S1_18891752 S3_100795476, S1_100584471 S1_100896936 and S2_10058799) were consistent in two different data sets. CONCLUSIONS: The findings from this study improve our understanding of the genetic control of adaptive and phenotypic traits in Nigerian taro. However, the study suggests further study on identification of local adaptive loci and GWAS through collection of more landraces throughout the country, and across different agro-ecologies.

DArTSeq SNP-based genetic diversity and population structure studies among taro [(Colocasia esculenta (L.) Schott] accessions sourced from Nigeria and Vanuatu.

Taro is a valuable staple food crop among resource-poor rural people in countries such as Nigeria and Ghana, among others. Characterization of genetic diversity is a prerequisite for proper management of breeding programs and conservation of genetic resources. Two hundred seventy one taro accessions obtained from Nigeria and Vanuatu were genotyped using DArTseq-based SNP markers with the objectives of investigating the genetic diversity and population structure. In the analysis, 10,391 SNP markers were filtered from the sequence and used. The analysis revealed higher transition than transversion types of SNPs in the ratio of 1.43:1. The polymorphism ranged from 0.26 to 0.29 for the markers, indicating moderate genetic diversity. A model-based Bayesian clustering analysis of taro accessions yielded five subgroups and revealed the admixture situation in 19.19% of all accessions in the study. Vanuatu taro accessions exhibited more genetic diversity than Nigerian taro accessions. The population diversity estimate (PhiPt) was relatively higher (0.52) for accessions originating from Vanuatu than for Nigerian accessions. Analysis of molecular variance (AMOVA) revealed that most variation existed among individuals within a population at 52%. Nei's genetic distance showed that relatedness is based on geographical proximity. Collection of taro genetic resources should give more emphasis to within regions to utilize diversity in taro breeding program. This study also demonstrated the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in taro. The genotypic markers provided in this study are useful for association mapping studies.

Genetic characterization and quantitative trait relationship using multivariate techniques reveal diversity among tomato germplasms.

Tomato accessions collected from different sources were evaluated to study their diversity, genotype-traits association, as well as pinpoint most selective trait(s) in a controlled environment in Jimma, Ethiopia. The two terms pot experiments were carried out in randomized complete block design with three replications. The genotype-trait (GT) biplot revealed high percentage variability above 70% in related growth traits for the first and second principal components (PC) summed up, in the two trials, whereas related floral and fruit traits association indicated medium to high (55%-65%) total explained variations in both seasons. It further showed that 'wild parent', 'CLN2498D', 'CLN2498F', 'UC Dan India', 'Ruma', 'PT4722A', 'CLN2679F', 'CLN2585C' and 'CLN2585D' were the best performers in most of the related growth, floral, and fruit traits in those seasons. Principal component analysis showed that traits, such as plant height, number of branches, leaves, nodes, internodes, stem girth, style length, stigma length and diameter, flower length and width, number of flowers per truss, number of fruits per truss, and fruit weight per plant, in the first dimension were positively related to yield and consistent with high loading factors in both seasons and could be underpinned highly important in breeding for increased fruit yield. Clustering and its comparison of means showed that 'CLN2498D', 'PT4722A', 'Ruma', 'Tropimech', and 'UC Dan India' of cluster I in both trials expressed the best traits including related growth, floral, and fruit traits. Therefore, selection for any trait would favor accessions in this cluster.

Genome properties of key oil palm (Elaeis guineensis Jacq.) breeding populations.

A good knowledge of the genome properties of the populations makes it possible to optimize breeding methods, in particular genomic selection (GS). In oil palm (Elaeis guineensis Jacq), the world's main source of vegetable oil, this would provide insight into the promising GS results obtained so far. The present study considered two complex breeding populations, Deli and La Mé, with 943 individuals and 7324 single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Linkage disequilibrium (LD), haplotype sharing, effective size (Ne), and fixation index (Fst) were investigated. A genetic linkage map spanning 1778.52 cM and with a recombination rate of 2.85 cM/Mbp was constructed. The LD at r2=0.3, considered the minimum to get reliable GS results, spanned over 1.05 cM/0.22 Mbp in Deli and 0.9 cM/0.21 Mbp in La Mé. The significant degree of differentiation existing between Deli and La Mé was confirmed by the high Fst value (0.53), the pattern of correlation of SNP heterozygosity and allele frequency among populations, and the decrease of persistence of LD and of haplotype sharing among populations with increasing SNP distance. However, the level of resemblance between the two populations over short genomic distances (correlation of r values between populations >0.6 for SNPs separated by <0.5 cM/1 kbp and percentage of common haplotypes >40% for haplotypes <3600 bp/0.20 cM) likely explains the superiority of GS models ignoring the parental origin of marker alleles over models taking this information into account. The two populations had low Ne (<5). Population-specific genetic maps and reference genomes are recommended for future studies.

Genetic diversity and population structure of an African yam bean (Sphenostylis stenocarpa) collection from IITA GenBank.

African yam bean, AYB (Sphenostylis stenocarpa), is an underutilized legume of tropical Africa. AYB can boost food and nutritional security in sub-Saharan Africa through its nutrient-rich seeds and tubers. However, inadequate information on germplasm with desirable agro-morphological traits, including insufficient data at the genomic level, has prevented the full exploitation of its food and breeding potentials. Notably, assessing the genetic diversity and population structure in a species is a prerequisite for improvement and eventual successful exploitation. The present study evaluated the population structure and genetic diversity of 169 accessions from the International Institute of Tropical Agriculture (IITA) collection using 26 phenotypic characters and 1789 single nucleotide polymorphism (SNP) markers. The phenotypic traits and SNP markers revealed their usefulness in uniquely distinguishing each AYB accession. The hierarchical cluster of phenotypes grouped accessions into three sub-populations; SNPs analysis also clustered the accessions into three sub-populations. The genetic differentiation (FST) among the three sub-populations was sufficiently high (0.14-0.39) and significant at P = 0.001. The combined analysis revealed three sub-populations; accessions in sub-population 1 were high yielding, members in sub-population 2 showed high polymorphic loci and heterozygosity. This study provides essential information for the breeding and genetic improvement of AYB.

Genomic selection in tropical perennial crops and plantation trees: a review.

To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01326-4.